Meta-Analysis of Single-Case Research via Multilevel Models: Fundamental Concepts and Methodological Considerations.
Multilevel modeling gives you a clear, four-step recipe to turn stacks of single-case studies into one trustworthy effect.
01Research in Context
What this study did
Moeyaert et al. (2020) wrote a how-to guide. They show you how to mash many single-case studies into one number with multilevel models.
The paper gives four clear steps. You extract data, build the model, check graphs, and write it up.
What they found
The authors found the math is doable. Free software like R can run the model.
They show pictures that let you see if each original study fits the final average.
How this fits with other research
Young (2018) did a similar trick. He used multilevel logistic regression on discounting data, not meta-analysis. Both papers treat the model as a friendly tool, not a black box.
Neely et al. (2024) take the idea further. They tell you to buddy up with a data scientist and feed huge data sets to machine-learning code. Mariola stops at combining small SCED studies; Neely wants mountains of session data.
Slanzi et al. (2024) handle the front end. Their Countee app collects raw numbers on your phone. Mariola handles the back end: what to do with those numbers once you have piles of studies.
Why it matters
If you run many single-case projects, you can now merge them without scary statistics. Follow the four steps to write a quantitative literature review that journals respect. The guide keeps the math transparent so you can defend every move to reviewers or parents.
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02At a glance
03Original abstract
Multilevel modeling is an approach that can be used to summarize single-case experimental design (SCED) data. Multilevel models were developed to analyze hierarchical structured data with units at a lower level nested within higher level units. SCEDs use time series data collected from multiple cases (or subjects) within a study that allow researchers to investigate intervention effectiveness at the individual level and also to investigate how these individual intervention effects change over time. There is an increased interest in the field regarding how SCEDs can be used to establish an evidence base for interventions by synthesizing data from a series of intervention studies. Although using multilevel models to meta-analyze SCED studies is promising, application is often hampered by being potentially excessively technical. First, this article provides an accessible description and overview of the potential of multilevel meta-analysis to combine SCED data. Second, a summary of the methodological evidence on the performance of multilevel models for meta-analysis is provided, which is useful given that such evidence is currently scattered over multiple technical articles in the literature. Third, the actual steps to perform a multilevel meta-analysis are outlined in a brief practical guide. Fourth, a suggestion for integrating the quantitative results with a visual representation is provided.
Behavior modification, 2020 · doi:10.1177/0145445518806867